Synthesizing realistic virtual environments is one of the most researched topics in computer graphics and computer vision.
An important decision is how 3D shapes should be encoded and stored in memory.
Users usually choose between triangle meshes, voxel grids, implicit functions, and point clouds [1]}, [2]}.
Each representation has different advantages and disadvantages.
For efficient rendering of opaque surfaces, triangle meshes are commonly chosen.
Voxel grids are often used in volume rendering while implicit functions can be used to precisely describe nonlinear analytical surfaces.
Point clouds, on the other hand, have the advantage of being easy to use because no topology has to be considered, which makes them a good candidate for intermediate output or as a representation in a scientific context.
In the early 2000s, point cloud rendering, especially with point splatting, has been a well researched field in computer graphics [3]}.
In parallel, image-based rendering techniques gained increasing interest [4]}. Based on a coarse, reconstructed 3D model, as well as a registered set of images of an object, novel views are synthesized.
These approaches suffer from imprecision in the input, for example, ghosting artifacts appear if the geometry contain holes or the input images are not perfectly aligned.
Neural image based rendering approaches such as [5]} use neural networks to remove these artifacts and can generate photo-realistic novel views of unprecedented quality.
Aliev et al. [6]} show that this is also possible by pairing a traditional point rasterizer with a deep neural network.
This is especially beneficial in the field of 3D reconstruction because dense point clouds are often the initial output.
An unnecessary and potentially erroneous triangulation can therefore be skipped and the reconstructed scene directly visualized.
